An Approach Towards the Least-Squares Method for Simple Linear Regression

Year 2022, Volume: 2 Issue: 2, 38 - 44, 23.09.2022

Hasan Halit Tali , Ceren Çelti

https://doi.org/10.54569/aair.1032607

Abstract

This study approaches the least-squares method for simple linear regression model. The least-squares line does not comply with the data when there are outliers that have deceptive effects on the results in the dataset. The study aims to develop a method for obtaining a line that complies more with the data when there are outliers in the dataset.

Keywords

Applied mathematics, Machine learning, Simple linear regression, Least-Squares method, Outliers

References

• Miller I, Miller M. Mathematical Statistics, Prenttice-Hall, Inc, 1999 (Çev. Ümit Şenesen John E. Freund’dan Matematiksel İstatistik, Literatür Yayıncılık. 2007).
• Arslan İ. Python ile Veri Bilimi, Pusula Yayıncılık, Türkiye, 2020.
• Rousseeuw PJ, Leroy AM. Robust regression and outlier detection. John Wiley & Sons, 1987.
• Attaway S. Matlab A Practical Introduction to Programming and Problem Solving. 5th ed. Cambridge, USA, Butterwoth-Heinmann, 2019.
• Kubat C. Matlab Yapay Zeka ve Mühendislik Uygulamaları. 5. Baskı, İstanbul, Türkiye, Abaküs Kitap Yayın, 2021.
• Güneş A, Yıldız K. Matlab Matematik ve Grafik Programlama Dili. İstanbul, Türkiye, Türkmen Kitabevi, 1997.
• Verardi V, Croux C. “Robust regression in Stata”. The Stata Journal, 9(3), 439-453, 2009.
• Andersen R. Modern methods for robust regression (No. 152). Sage, 2008.